Analyses:

Annual avereage SWE

  • low swe 2004-2007 and 2018
  • Carrizo always has highest average SWE value, while Navajo mountain usually has the least

The annual average % contribution to the total annual average swe of all 6 mountain ranges:

  • Chuska: 0.81
  • Defiance Plateau 0.11
  • Black Mesa 0.06
  • Carrizo: 0.02
  • Navajo Mt 0.0023
  • Mt Powell 0.0049

Daily SWE variability (November - April Water Year)

Monthly Mean SWE variability (November - April Water Year)

All monthly mean

Correlation matrix of all monthly mean swe

- Black Mesa to Mt. Powell, Black Mesa to Defiance Plateau and Defiance Plateau and Mt Powell all have the highest correlations between each other - Least correlated are Black Mesa and Navajo Mt, Navajo Mt and Mt Powell, Navajo Mt and Defiance Plateau

Monthly Median

Monthly Anomalies (November - April Water Year)

Correlations Between locations

Weekly mean correlation November - April

Pearson’s Correlation R squared:

  • Chuska vs Black Mesa: 0.46

  • Carrizo vs Chuska: 0.63

  • Carrizo vs Black Mesa: 0.73

Chuska vs Black Mesa R squared: 0.4634808

Chuska vs Carrizo R squared: 0.631619

Black Mesa vs Carrizo R squared: 0.7250093

All weekly mean

Month specific time series

November

December

January

February

March

April

November - April Averaged SWE values

## Warning: Removed 1 rows containing non-finite values (stat_boxplot).

  • Chuska and Defiance plateau frequently have the higest av swe values, navajo mt usually has the least.
  • most everything is melted by april
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).

Total SWE for each region analyses

all_swe_total <- read_csv("winter_wy/all_swe_total.csv")
## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   car_total = col_double(),
##   ch_total = col_double(),
##   bm_total = col_double(),
##   dp_total = col_double(),
##   nm_total = col_double(),
##   mp_total = col_double(),
##   mt_range_sum_total = col_double()
## )
# monthly mean totals for each region

SWE compared to Suzanne’s SCA

## Parsed with column specification:
## cols(
##   `Water Year` = col_integer(),
##   Month = col_integer(),
##   rMonth = col_integer(),
##   cal_year = col_integer(),
##   Fday = col_integer(),
##   MeanSCA = col_double(),
##   `MeanSCA (m2)` = col_double(),
##   `MeanSCA (km2)` = col_integer(),
##   Anomaly = col_integer(),
##   `Anomaly by Month` = col_integer(),
##   X11 = col_character()
## )

SCA Anomaly was scaled down by a factor of 10 to improve analysis

  • SCA anomalies only somewhat track to SWE anomalies